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                <h2 id="为什么要有一致性哈希算法"><a href="#为什么要有一致性哈希算法" class="headerlink" title="为什么要有一致性哈希算法"></a>为什么要有一致性哈希算法</h2><p>我们首先想象一个这样的场景，如果我们有3台缓存服务器用于缓存图片，为这3台服务器编号为0号、1号、2号、现在有3万张图片需要缓存在这3台服务器上，我们肯定希望这些图片被均匀的分散到这3台服务器上，以便能够分摊缓存的压力。那么我们应该怎么做呢？</p>
<p>如果我们随意的将这3万张图片缓存在这3台服务器上，可以满足我们的需求吗？可以，但是如果这样做，当我们需要访问某个缓存项时，则需要遍历3台缓存服务器，从3万张缓存图片中找到我们需要访问的那张图片，遍历的过程效率太低，时间太长，当我们找到需要访问的图片时，时长可能是不能被接受的，也就失去了缓存的意义，缓存的目的就是提高速度，改善用户体验，减轻后端服务器压力。那我们该怎么办呢？</p>
<p>原始的做法就是对缓存项的键进行哈希，将哈希后的结果对缓存服务器的数量进行取模，通过取模后的结果，决定缓存项将会缓存在哪台服务器上。比如将图片的名称作为访问图片的 key（假设图片名称是不重复的），那么我们就可以用计算出来的哈希值对服务器数量取余，这样就可以将图片很好的散列到不同的服务器上面了，满足了我们的需求。但是这样真的就没有问题了吗？</p>
<p>假设现在服务器数量由3台变成了4台，那每个图片的哈希值再对服务器数量进行取余操作，余数肯定是不同的（当有一台或多台服务器宕机时也是如此），这样造成的一个问题就是所有缓存项的位置都要发生变动，缓存在此时就是失效的状态，那么大量的缓存在同一时间失效就会引起缓存的雪崩，所有的请求就会打到数据库上，造成数据库服务器宕机，系统就被压垮了。这我们肯定是不能接受的。</p>
<p>总结一下上面普通的哈希算法（取模运算）会出现的问题：</p>
<ul>
<li>当服务器数量发生变化的时候，几乎所有缓存的位置都会发生变化</li>
<li>当服务器数量发生变化的时候，会引起缓存的雪崩，引起系统的崩溃</li>
</ul>
<p>那我们该怎么解决上述的问题呢，于是一致性哈希算法就登场了。</p>
<h2 id="一致性哈希算法的原理"><a href="#一致性哈希算法的原理" class="headerlink" title="一致性哈希算法的原理"></a>一致性哈希算法的原理</h2><p>一致性哈希算法（Consistent Hashing）最早被用来解决分布式 Cache 问题的，它的原理也是比较简单的，下面就有我带你揭开一致性哈希算法的真面目。</p>
<p>一致性哈希将整个哈希值空间组织成一个虚拟的圆环，如假设某个哈希函数的值空间是0~2^32 - 1（即哈希值是一个32位无符号整形），整个哈希空间环如下：</p>
<img src="https://cdn.jsdelivr.net/gh/kuangtianyu/img/img/image-20220327123240656.png" alt="image-20220327123240656" style="zoom:80%;" />

<p>整个空间按顺时针方向组织，0和2^32 - 1在零点钟方向重合。下一步就可以将各个服务器通过哈希函数计算出哈希值，可以选择服务器的 IP 或者主机名进行哈希，这样就可以确定每台机器在哈希环上的位置，每台服务器就是一个环上的节点，假设将四台服务器使用 IP 地址进行哈希后在环空间中的位置如下：</p>
<p><img src="https://cdn.jsdelivr.net/gh/kuangtianyu/img/img/image-20220327123254435.png" alt="image-20220327123254435" style="zoom:80%;" /><img src="https://cdn.jsdelivr.net/gh/kuangtianyu/img/img/image-20220327123307813.png" alt="image-20220327123307813"></p>
<p>接下来使用如下算法定位数据访问到响应的服务器：将数据 key 使用相同的哈希函数（就是计算服务器节点的哈希函数）算出哈希值，并确定在环上的位置，从此位置顺时针“行走”，第一台遇到的服务器就是其应该定位到的服务器。例如我们有Object A、Object B、Object C、Object D四个数据对象，经过哈希计算后，在环空间上的位置如下：</p>
<p><img src="https://cdn.jsdelivr.net/gh/kuangtianyu/img/img/image-20220327123254435.png" alt="image-20220327123254435" style="zoom:80%;" /><img src="https://cdn.jsdelivr.net/gh/kuangtianyu/img/img/image-20220327123307813.png" alt="image-20220327123307813"><img src="https://cdn.jsdelivr.net/gh/kuangtianyu/img/img/image-20220327123318487.png" alt="image-20220327123318487"></p>
<p>根据一致性哈希算法，数据A会被定为到Node A上，B被定为到Node B上，C被定为到Node C上，D被定为到Node D上。下面分析一下容错性和可扩展性。</p>
<p>现假设Node C不幸宕机，可以看到此时对象A、B、D不会受到影响，只有C对象被重定位到Node D。一般的，在一致性哈希算法中，如果一台服务器不可用，则受影响的数据仅仅是此服务器在环中逆时针“行走”的数据，这些数据都会被重新定位到该服务器顺时针“行走”的第一台服务器上面。这也是一致性哈希算法相比于普通的哈希算法的优势，只有一个区间中的数据会收到影响，其他的数据不会受到影响，而普通的哈希算法会导致所有数据收到影响，缓存项的数据都会被改变。如图所示：</p>
<p><img src="https://cdn.jsdelivr.net/gh/kuangtianyu/img/img/image-20220327123254435.png" alt="image-20220327123254435" style="zoom:80%;" /><img src="https://cdn.jsdelivr.net/gh/kuangtianyu/img/img/image-20220327123307813.png" alt="image-20220327123307813"><img src="https://cdn.jsdelivr.net/gh/kuangtianyu/img/img/image-20220327123318487.png" alt="image-20220327123318487"><img src="https://cdn.jsdelivr.net/gh/kuangtianyu/img/img/image-20220327123330453.png" alt="image-20220327123330453"></p>
<p>再考虑另一种情况，如果在系统中增加一台服务器E，E定位到了B、C节点之间，会怎么样呢？那么对象C将会定位到节点E，而不是节点C了，如图所示：</p>
<p><img src="https://cdn.jsdelivr.net/gh/kuangtianyu/img/img/image-20220327123330453.png" alt="image-20220327123330453"></p>
<p>此时对象Object A、B、D不受影响，只有对象C需要重定位到新的Node E 。一般的，在一致性哈希算法中，如果增加一台服务器，则受影响的数据仅仅是新服务器到其环空间中前一台服务器（即沿着逆时针方向行走遇到的第一台服务器）之间数据，其它数据也不会受到影响。</p>
<p>综上所述，一致性哈希算法对于节点的增减都只需重定位环空间中的一小部分数据，具有较好的容错性和可扩展性。那么一致性哈希算法就没有缺点了吗？</p>
<p>一致性哈希算法在服务节点太少时，容易因为节点分部不均匀而造成数据倾斜问题。例如系统中只有两台服务器，其环分布如下：</p>
<img src="https://cdn.jsdelivr.net/gh/kuangtianyu/img/img/image-20220327123348544.png" alt="image-20220327123348544" style="zoom:80%;" />

<p>此时必然造成大量数据集中到Node A上，而只有极少量会定位到Node B上。为了解决这种数据倾斜问题，一致性哈希算法引入了虚拟节点机制，即对每一个服务节点计算多个哈希，每个计算结果位置都放置一个此服务节点，称为虚拟节点。具体做法可以在服务器ip或主机名的后面增加编号来实现。例如上面的情况，可以为每台服务器计算三个虚拟节点，于是可以分别计算 “Node A#1”、“Node A#2”、“Node A#3”、“Node B#1”、“Node B#2”、“Node B#3”的哈希值，于是形成六个虚拟节点：</p>
<img src="https://cdn.jsdelivr.net/gh/kuangtianyu/img/img/image-20220327123422998.png" alt="image-20220327123422998" style="zoom:80%;" />

<p>同时数据定位算法不变，只是多了一步虚拟节点到实际节点的映射，例如定位到“Node A#1”、“Node A#2”、“Node A#3”三个虚拟节点的数据均定位到Node A上。这样就解决了服务节点少时数据倾斜的问题。在实际应用中，通常将虚拟节点数设置为32甚至更大，因此即使很少的服务节点也能做到相对均匀的数据分布。</p>
<p>到此一致性哈希算法的原理到此就结束了，下面看看一致性哈希算法的性质。</p>
<h2 id="一致性哈希算法的性质"><a href="#一致性哈希算法的性质" class="headerlink" title="一致性哈希算法的性质"></a>一致性哈希算法的性质</h2><p>考虑到分布式系统每个节点都有可能失效，并且新的节点很可能动态的增加进来，如何保证当系统的节点数目发生变化时仍然能够对外提供良好的服务，这是值得考虑的，尤其实在设计分布式缓存系统时，如果某台服务器失效，对于整个系统来说如果不采用合适的算法来保证一致性，那么缓存于系统中的所有数据都可能会失效（即由于系统节点数目变少，客户端在请求某一对象时需要重新计算其hash值（通常与系统中的节点数目有关），由于hash值已经改变，所以很可能找不到保存该对象的服务器节点），因此一致性hash就显得至关重要，良好的分布式cahce系统中的一致性hash算法应该满足以下几个方面：</p>
<p><strong>平衡性(Balance)</strong></p>
<p>平衡性是指哈希的结果能够尽可能分布到所有的缓冲中去，这样可以使得所有的缓冲空间都得到利用。很多哈希算法都能够满足这一条件。</p>
<p><strong>单调性(Monotonicity)</strong></p>
<p>单调性是指如果已经有一些内容通过哈希分派到了相应的缓冲中，又有新的缓冲区加入到系统中，那么哈希的结果应能够保证原有已分配的内容可以被映射到新的缓冲区中去，而不会被映射到旧的缓冲集合中的其他缓冲区。简单的哈希算法往往不能满足单调性的要求，如最简单的线性哈希：x = (ax + b) mod (P)，在上式中，P表示全部缓冲的大小。不难看出，当缓冲大小发生变化时(从P1到P2)，原来所有的哈希结果均会发生变化，从而不满足单调性的要求。哈希结果的变化意味着当缓冲空间发生变化时，所有的映射关系需要在系统内全部更新。而在P2P系统内，缓冲的变化等价于Peer加入或退出系统，这一情况在P2P系统中会频繁发生，因此会带来极大计算和传输负荷。单调性就是要求哈希算法能够应对这种情况。</p>
<p><strong>分散性(Spread)</strong></p>
<p>在分布式环境中，终端有可能看不到所有的缓冲，而是只能看到其中的一部分。当终端希望通过哈希过程将内容映射到缓冲上时，由于不同终端所见的缓冲范围有可能不同，从而导致哈希的结果不一致，最终的结果是相同的内容被不同的终端映射到不同的缓冲区中。这种情况显然是应该避免的，因为它导致相同内容被存储到不同缓冲中去，降低了系统存储的效率。分散性的定义就是上述情况发生的严重程度。好的哈希算法应能够尽量避免不一致的情况发生，也就是尽量降低分散性。</p>
<p><strong>负载(Load)</strong></p>
<p>负载问题实际上是从另一个角度看待分散性问题。既然不同的终端可能将相同的内容映射到不同的缓冲区中，那么对于一个特定的缓冲区而言，也可能被不同的用户映射为不同的内容。与分散性一样，这种情况也是应当避免的，因此好的哈希算法应能够尽量降低缓冲的负荷。</p>
<p><strong>平滑性(Smoothness)</strong></p>
<p>平滑性是指缓存服务器的数目平滑改变和缓存对象的平滑改变是一致的。</p>
<blockquote>
<p>巨人的肩膀：</p>
<p><a target="_blank" rel="noopener" href="https://www.cnblogs.com/lpfuture/p/5796398.html">https://www.cnblogs.com/lpfuture/p/5796398.html</a></p>
<p><a target="_blank" rel="noopener" href="https://www.zsythink.net/archives/1182">https://www.zsythink.net/archives/1182</a></p>
</blockquote>

                
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